{"created":"2025-01-19T01:35:52.251298+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00234181","sets":["1164:4619:11539:11642"]},"path":["11642"],"owner":"44499","recid":"234181","title":["マルチスケールな検出領域を用いた改良型ODAMによる可視化結果の解釈容易性向上"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-05-08"},"_buckets":{"deposit":"ea12570c-ee73-446d-b288-135e7563c2e6"},"_deposit":{"id":"234181","pid":{"type":"depid","value":"234181","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"マルチスケールな検出領域を用いた改良型ODAMによる可視化結果の解釈容易性向上","author_link":["637622","637624","637625","637623"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"マルチスケールな検出領域を用いた改良型ODAMによる可視化結果の解釈容易性向上"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"卒論スポットライトセッション (CVIM)","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2024-05-08","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"中部大学"},{"subitem_text_value":"中部大学"},{"subitem_text_value":"中部大学"},{"subitem_text_value":"中部大学"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Chubu University","subitem_text_language":"en"},{"subitem_text_value":"Chubu University","subitem_text_language":"en"},{"subitem_text_value":"Chubu University","subitem_text_language":"en"},{"subitem_text_value":"Chubu University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/234181/files/IPSJ-CVIM24238050.pdf","label":"IPSJ-CVIM24238050.pdf"},"date":[{"dateType":"Available","dateValue":"2026-05-08"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-CVIM24238050.pdf","filesize":[{"value":"8.6 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"20"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"41e6dc6e-b084-4683-ac80-32179b709c9a","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2024 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"仲井, 悠真"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"平川, 翼"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"山下, 隆義"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"藤吉, 弘亘"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11131797","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8701","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"深層学習モデルによる物体検出は，自動運転や医療画像解析等の分野で幅広く利用されている．特に Transformer を用いた物体検出法はその高い検出精度で注目されているが，モデルの検出結果に対する判断根拠は不明瞭であり，ブラックボックスとされている．この問題に対し，勾配ベースで物体検出結果に対して判断根拠を可視化する手法として，Object Detector Activation Maps (ODAM) が提案されている．ODAM は検出領域に対するアテンションマップを出力するが，ノイズに敏感であることから，検出した物体以外の領域を強調することがある．そこで本研究では，ODAM の解釈容易性の向上を目的とし，マルチスケールな検出領域を用いた改良型 ODAM を提案する．提案手法では，ODAM に与える検出領域が，可視化結果に大きな影響を及ぼすという性質を利用する．具体的には，検出領域の大きさの変化によって検出物体に注視した可視化結果になるという性質を利用し，異なる拡張率を持つ Bounding Box での可視化結果を平均する．これにより，着目する領域の変動を抑制し，可視化結果の忠実度を維持しつつ解釈容易性を高める．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"7","bibliographic_titles":[{"bibliographic_title":"研究報告コンピュータビジョンとイメージメディア（CVIM）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2024-05-08","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"50","bibliographicVolumeNumber":"2024-CVIM-238"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"links":{},"id":234181,"updated":"2025-01-19T09:52:36.059546+00:00"}